{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T1nBdtXIQsC7"
      },
      "source": [
        "## Use MetaCLIP2 for Image Search"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bkMtFcLQF_8g"
      },
      "source": [
        "Have you ever wondered how text search in your gallery works?\n",
        "\n",
        "Meta released MetaCLIP2, a model that can understand both text and images in many languages! In this notebook we will use it on free GPU to search images from text queries."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BiOtEgRaG4ds"
      },
      "source": [
        "![](https://huggingface.co/datasets/merve/vlm_test_images/resolve/main/image_search.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9e5VWlOdGPC4"
      },
      "source": [
        "Typical workflow is the following:\n",
        "\n",
        "1 **Offline Indexing:** Pass all your images through the model's image part and keep indexed image embeddings in a vector DB (so you know which embedding is which image). This is done once, hence called offline indexing, and takes time.\n",
        "\n",
        "2.1. **Online Inference:** Pass the text query through model's text part to get the query text embedding.\n",
        "\n",
        "2.2. **Similarity & Look-up:** Run similarity between the text embedding and image embeddings and get the most similar image embedding, then look-up from the vector DB."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4tCnCuTnHk8_"
      },
      "source": [
        "Let's install packages, download MetaCLIP2 and my dataset (which is bunch of food images). We'll use FAISS-GPU to keep our images."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "diDfx6YeGTH3"
      },
      "outputs": [],
      "source": [
        "!pip install -q faiss-gpu-cu12"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "RZTdwsHOGjK-"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "ds = load_dataset(\"merve/food\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vYcGOaWMGr6N",
        "outputId": "90f002d3-24f9-4094-978b-597894273de3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['image'],\n",
              "        num_rows: 16\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "ds"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's load the model. FAISS accepts vectors as float32 so we load the model in that precision."
      ],
      "metadata": {
        "id": "6Xg1YEgiupFn"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T1dAaJ6ON1Bu"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from transformers import AutoProcessor, AutoModel, infer_device\n",
        "\n",
        "\n",
        "device = infer_device()\n",
        "model = AutoModel.from_pretrained(\"facebook/metaclip-2-worldwide-giant\", torch_dtype=torch.float32, attn_implementation=\"sdpa\").to(device)\n",
        "processor = AutoProcessor.from_pretrained(\"facebook/metaclip-2-worldwide-giant\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uwC0CJXDPRAC"
      },
      "source": [
        "Dataset indexing function ⤵️ Here we just load the image, preprocess and pass through model to get the embedding. After, we return embedding as list so we can pass it to FAISS.\n",
        "\n",
        "We initialize the index and embed images and add vector to the index. We initialize the index with the projection dim we find in the model configuration."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.config.projection_dim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6_-kV2o-wF4z",
        "outputId": "4c7aef8d-78ec-4285-dda2-160ec9450446"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1280"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "xGjQczdZPVfO"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import faiss\n",
        "import numpy as np\n",
        "\n",
        "def add_vector(embedding, index):\n",
        "    vector = embedding.detach().cpu().numpy()\n",
        "    vector = np.float32(vector)\n",
        "    faiss.normalize_L2(vector)\n",
        "    index.add(vector)\n",
        "\n",
        "def embed_metaclip(image):\n",
        "    with torch.no_grad():\n",
        "        inputs = processor(images=image, return_tensors=\"pt\").to(device)\n",
        "        image_features = model.get_image_features(**inputs)\n",
        "        return image_features\n",
        "\n",
        "\n",
        "index = faiss.IndexFlatL2(1280)\n",
        "\n",
        "for elem in ds[\"train\"]:\n",
        "  clip_features = embed_metaclip(elem[\"image\"])\n",
        "  add_vector(clip_features,index)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t7_4pWbbRnzg"
      },
      "source": [
        "Now we write online inference. Basically we will take a text prompt and find closest k number of images in our indexed database."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "upWvVU3OGuAI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "26eadd64-93dc-434a-846f-2bee8d495c86"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[15,  1,  4]])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "prompt=\"an ice-cream\"\n",
        "\n",
        "text_token = processor.tokenizer([prompt], return_tensors=\"pt\").to(device)\n",
        "text_features = model.get_text_features(**text_token)\n",
        "\n",
        "text_features = text_features.detach().cpu().numpy()\n",
        "text_features = np.float32(text_features)\n",
        "faiss.normalize_L2(text_features)\n",
        "\n",
        "distances, indices = index.search(text_features, 3)\n",
        "indices"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sjjelh6BSMNf"
      },
      "source": [
        "Let's put all of these to action now. We will encode our dataset, create the index, write it to FAISS. Optionally you can choose to save the FAISS DB.\n",
        "\n",
        "Then we will search our queries and find best images."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "pVYk4W5HNYjT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "4727c5bc-9033-4ce0-bef0-441203eee426"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=100x133>"
            ],
            "image/png": 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\n",
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=100x133>"
            ],
            "image/png": 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\n",
            "image/jpeg": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=100x133>"
            ],
            "image/png": 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\n",
            "image/jpeg": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from PIL import Image\n",
        "for elem in indices[0]:\n",
        "  elem = int(elem)\n",
        "  image = ds[\"train\"][elem][\"image\"]\n",
        "\n",
        "  # downscale\n",
        "  width = 100\n",
        "  ratio = (width / float(image.size[0]))\n",
        "  height = int((float(image.size[1]) * float(ratio)))\n",
        "  img = image.resize((width, height), Image.Resampling.LANCZOS)\n",
        "  display(img)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In our gallery there were only two ice cream images, but since we asked the model to return 3 results, it returned an irrelevant image. We can filter them out using \"distance\" which is how far the image to the text query is. As you can see, it's the farthest one."
      ],
      "metadata": {
        "id": "v5yl6NflxXY0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "distances"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bbnemg_zxQZP",
        "outputId": "f6c5c8da-9884-49d7-8737-3ff1a932131a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.6205274, 1.6923535, 1.7776461]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "One cool thing is that this model can do it in multiple languages as well. As you can see it returns the same result when given the same query in French."
      ],
      "metadata": {
        "id": "93tWAROayqqj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prompt=\"une glace\"\n",
        "\n",
        "text_token = processor.tokenizer([prompt], return_tensors=\"pt\").to(device)\n",
        "text_features = model.get_text_features(**text_token)\n",
        "\n",
        "text_features = text_features.detach().cpu().numpy()\n",
        "text_features = np.float32(text_features)\n",
        "faiss.normalize_L2(text_features)\n",
        "\n",
        "distances, indices = index.search(text_features, 3)\n",
        "indices"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U5JaRiHsytS-",
        "outputId": "721d40e8-253e-470f-ff09-44883b36c1d6"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[15,  1,  4]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}